Author classification using transfer learning and predicting stars in co‐author networks

Summary The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the...

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Bibliographic Details
Published in:Software, practice & experience practice & experience, 2021-03, Vol.51 (3), p.645-669
Main Authors: Abbasi, Rashid, Kashif Bashir, Ali, Chen, Jianwen, Mateen, Abdul, Piran, Jalil, Amin, Farhan, Luo, Bin
Format: Article
Language:English
Subjects:
Algorithms
author classification
Classification
Domains
Machine learning
semantic web
social network
transfer learning
Unstructured data
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Summary:Summary The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real‐world networks showed that ACTL, Node‐based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain‐based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods.
ISSN:0038-0644
1097-024X
DOI:10.1002/spe.2884